docs-v2/content/flux/v0.x/stdlib/experimental/histogram.md

title	description	menu	weight	aliases	related	flux/v0.x/tags	introduced
experimental.histogram() function	The `experimental.histogram()` function approximates the cumulative distribution of a dataset by counting data frequencies for a list of bins.	flux_0_x_ref name parent experimental.histogram experimental	302	/influxdb/v2.0/reference/flux/stdlib/experimental/histogram/ /influxdb/cloud/reference/flux/stdlib/experimental/histogram/	/influxdb/v2.0/query-data/flux/histograms/ /flux/v0.x/stdlib/universe/histogram/	transformations	0.112.0

The experimental.histogram() function approximates the cumulative distribution of a dataset by counting data frequencies for a list of bins. A bin is defined by an upper bound where all data points that are less than or equal to the bound are counted in the bin. Bin counts are cumulative.

import "experimental"

experimental.histogram(
    bins: [50.0, 75.0, 90.0],
    normalize: false,
)

Output schema

experimental.histogram() outputs a single table for each input table. Each output table represents a unique histogram. Output tables have the same group key as the corresponding input table.

The function does the following:

• Drops columns that are not part of the group key.
• Adds an le column to store upper bound values.
• Stores bin counts in the _value column.

Parameters

bins

({{< req >}}) A list of upper bounds to use when computing the histogram frequencies, including the maximum value of the data set. This value can be set to positive infinity if no maximum is known.

Bin helper functions

The following helper functions can be used to generated bins.

linearBins()
logarithmicBins()

normalize

Convert count values into frequency values between 0 and 1. Default is false.

{{% note %}} Normalized histograms cannot be aggregated by summing their counts. {{% /note %}}

tables

Input data. Default is piped-forward data (<-).

Examples

Histogram with dynamically generated bins

import "experimental"

data
    |> experimental.histogram(bins: linearBins(start: 0.0, width: 20.0, count: 5))

Input data

{{< flex >}} {{% flex-content %}}

_time	host	_value
2021-01-01T00:00:00Z	host1	33.4
2021-01-01T00:01:00Z	host1	57.2
2021-01-01T00:02:00Z	host1	78.1
2021-01-01T00:03:00Z	host1	79.6
{{% /flex-content %}}
{{% flex-content %}}
_time	host	_value
:-----	:----	------:
2021-01-01T00:00:00Z	host2	10.3
2021-01-01T00:01:00Z	host2	19.8
2021-01-01T00:02:00Z	host2	54.6
2021-01-01T00:03:00Z	host2	56.9
{{% /flex-content %}}
{{< /flex >}}

Output data

{{< flex >}} {{% flex-content %}}

host	le	_value
host1	0	0
host1	20	0
host1	40	1
host1	60	2
host1	80	4
host1	+Inf	4
{{% /flex-content %}}
{{% flex-content %}}
host	le	_value
:----	--:	------:
host2	0	0
host2	20	2
host2	40	2
host2	60	4
host2	80	4
host2	+Inf	4
{{% /flex-content %}}
{{< /flex >}}